Electric Load Forecasting By Support Vector Model

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Electric load forecasting by support vector model ...

    https://www.sciencedirect.com/science/article/pii/S0307904X08001844
    On the other hand, electric load forecasting model ought to contain several social factors to increase its explanation capabilities, i.e., multivariate forecasting models, such as social activities and seasonal factors could be introduced into the SVRIA model to forecast electric load.Cited by: 262

Electric load forecasting by support vector model ...

    https://www.researchgate.net/publication/222768707_Electric_load_forecasting_by_support_vector_model
    A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector …Author: Wei-Chiang Hong

Electricity Load Forecasting Using Support Vector ...

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3891232/
    Dec 26, 2013 · Electricity load forecasting is an important issue to operate the power system reliably and economically. In this study, to improve forecasting accuracy of electricity load forecasting using support vector regression (SVR), a firefly algorithm (FA) …Cited by: 55

Electric load forecasting by support vector model ...

    https://www.sciencedirect.com/science/article/abs/pii/S0307904X08001844
    Therefore, a rigid forecasting approach with strong general nonlinear mapping capabilities is essential. Support vector regression (SVR) applies the structural risk minimization principle to minimize an upper bound of the generalization errors, rather than minimizing the training errors which are used by ANNs.Cited by: 262

Support Vector Machine Model in Electricity Load Forecasting

    https://www.researchgate.net/publication/224677810_Support_Vector_Machine_Model_in_Electricity_Load_Forecasting
    With load forecasting at the top layer implemented by the recursive least square support vector machines (RLS-SVM) algorithm, discrete state-space equations are established to …

Short-term electrical load forecasting using the Support ...

    https://www.sciencedirect.com/science/article/pii/S0306261917302581
    Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings ... Fan SunElectric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing, 173 (2016), pp. 958-970. Google Scholar.Cited by: 91

Electric load forecasting by support vector model ...

    https://www.sciencedirect.com/science/article/pii/S0307904X08001844
    On the other hand, electric load forecasting model ought to contain several social factors to increase its explanation capabilities, i.e., multivariate forecasting models, such as social activities and seasonal factors could be introduced into the SVRIA model to forecast electric load.

Electric load forecasting by support vector model ...

    https://www.researchgate.net/publication/222768707_Electric_load_forecasting_by_support_vector_model
    A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector …

Electricity Load Forecasting Using Support Vector ...

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3891232/
    Dec 26, 2013 · Electricity load forecasting is an important issue to operate the power system reliably and economically. In this study, to improve forecasting accuracy of electricity load forecasting using support vector regression (SVR), a firefly algorithm (FA) …

Electric load forecasting by support vector model ...

    https://www.sciencedirect.com/science/article/abs/pii/S0307904X08001844
    Therefore, a rigid forecasting approach with strong general nonlinear mapping capabilities is essential. Support vector regression (SVR) applies the structural risk minimization principle to minimize an upper bound of the generalization errors, rather than minimizing the training errors which are used by ANNs.

Support Vector Machine Model in Electricity Load Forecasting

    https://www.researchgate.net/publication/224677810_Support_Vector_Machine_Model_in_Electricity_Load_Forecasting
    With load forecasting at the top layer implemented by the recursive least square support vector machines (RLS-SVM) algorithm, discrete state-space equations are established to …

Short-term electrical load forecasting using the Support ...

    https://www.sciencedirect.com/science/article/pii/S0306261917302581
    Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings ... Fan SunElectric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing, 173 (2016), pp. 958-970. Google Scholar.

Support vector machines for short‐term electrical load ...

    https://onlinelibrary.wiley.com/doi/abs/10.1002/er.787
    2016 International Conference on Electrical and Information Technologies (ICEIT), (2016). Malek Sarhani and Abdellatif El Afia Feature selection and parameter optimization of support vector regression for electric load forecasting 28810.1109/EITech.2016.75196082016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON), (2016).

Electric load forecasting by seasonal recurrent SVR ...

    https://www.sciencedirect.com/science/article/pii/S0360544211004634
    This investigation presents an electric load forecasting model which combines the seasonal recurrent support vector regression model with chaotic artificial bee colony algorithm (namely SRSVRCABC) to improve the forecasting performance.

Chaotic particle swarm optimization algorithm in a support ...

    https://www.sciencedirect.com/science/article/pii/S019689040800318X
    Thus, it is worth analyzing where these forecasts fail and how forecasting accuracy is improved. Based on authors’ series research on applications of support vector regression in electric load forecasting, SVR with evolutionary algorithms is a superior alternative to improve the load forecasting …

Electric load forecasting by complete ensemble empirical ...

    https://link.springer.com/article/10.1007/s11071-019-05252-7
    Sep 20, 2019 · Abstract. Accurate electric load forecasting can provide critical support to makers of energy policy and managers of power systems. The support vector regression (SVR) model can be hybridized with novel meta-heuristic algorithms not only to identify fluctuations and the nonlinear tendencies of electric loads, but also to generate satisfactory forecasts.



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